A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

Abstract

Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. It results in some inaccuracy. The paper proposes a fatigue driving detection algorithm based on facial multi-feature fusion combining driver characteristics. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, on the basis of the Dlib toolkit, we introduce the Eye Feature Vector(EFV) and Mouth Feature Vector(MFV), which are the evaluation parameters of the driver’s eye state and mouth state, respectively. Then, the driver identity information library is constructed by offline training, including driver eye state classifier library, driver mouth state classifier library, and driver biometric library. Finally, we construct the driver identity verification model and the driver fatigue assessment model by online assessment. After passing the identity verification, calculate the driver’s closed eyes time, blink frequency and yawn frequency to evaluate the driver’s fatigue state. In simulated driving applications, our algorithm detects the fatigue state at a speed of over 20fps with an accuracy of 95.10%.